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Challenges of Integrating Biophysical Information into Agricultural Sector Models Linking Biophysical and Economic Models of Biofuel Production and Environmental.

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Presentation on theme: "Challenges of Integrating Biophysical Information into Agricultural Sector Models Linking Biophysical and Economic Models of Biofuel Production and Environmental."— Presentation transcript:

1 Challenges of Integrating Biophysical Information into Agricultural Sector Models Linking Biophysical and Economic Models of Biofuel Production and Environmental Impacts November 13-14, 2008 Gleacher Center, Chicago IL. Daniel G. De La Torre Ugarte, Lixia Lambert, Burton English, Brad Wilson

2 POLYSYS Modules and Interaction Expected Returns & Available Acreage Acreage Allocation Based on Expected Returns Acreage, Production, Expenditures Export Use Domestic use Production Price Available for Domestic Consumption Food Use Feed Use Bioenery Use Total Use Price Value of Exports & Production Gov’t Payments Cash Receipts Gross & Net Realized Income Production Expenses (U.S.) Crop Demand (U.S.) Crop Demand Livestock (U.S.) Livestock (U.S.) Crop Supply (7 /305/ 3110 Regions) Crop Supply (7 /305/ 3110 Regions) (U.S.) Ag Income (U.S.) Ag Income

3 Our Initial Motivation Analysis of economic and environmental tradeoffs Sustainability context: erosion, N,P,K, chemical Economic tradeoffs: net returns, net farm income, government cost, price changes National and regional policy instruments Several sector models have integrated biophysical models since the mid 1980’s

4 Connection between economic and environmental analysis level Net Farm Income Net Returns Government Costs Prices Variability Erosion N, P, K Leaching Chemical Risk Water use Carbon Embodied Energy LINKS

5 Interaction with Environmental Module Crop Supply (305 Regions) Crop Demand (U.S.) Livestock (U.S.) Ag Income (U.S.) Environmental (305 Regions) Nitrogen Runoff, Leaching Phosphorus Runoff, Leaching Chemical Risk Index* Other Environmental Variables Soil Erosion Yield Impacts * Chemical Risk Index from Kovach, J., C. Petzoldt, J. Degni, and J. Tette (1992).

6 Integration of EPIC Rotations APAC Budgeting System POLYSYS Land Allocation by Soil Type Rotations Soils STATSGO EPIC Environmental Indicators

7 POLYSYS Regions (305) ASDs

8 Levels of Aggregation Nation State Farm Agricultural Statistic District USDA Region

9 Changes in Chemical Risk

10 Environmental Impacts from Maximizing Alternative Practices (ACE)

11 Main Challenges and decisions Geographic aggregation analytical level What to include: Crops, rotations, practices, land, soils, etc. Diverse resolution for economic and environmental data Average environmental impacts vs. dynamic environmental impacts Shrinking agricultural economic databases

12 Analytical Resolution Economic: – Lower resolution better economic data more reliable output – High resolution lower reliability of economic output Environmental: – Lower resolution, too much aggregation, less significance of environmental impacts – Higher resolution better significance of environmental output Compromise: objectives, data, computer power, $$$

13 Changes in Soil Carbon*: No LANDSAT - LANDSAT *POLYSYS estimates Carbon changes based on West, Marland, King, Post, Jain, and K Andrasko (2003)

14 Comprehensiveness Land: cropland, pasture, idle, forest – Begins with research objectives, driven by complexity of the forthcoming issues and availability of biophysical data Crops, rotations, livestock activities, forest – Economically/environmentally meaningful for resources, region, nation, market. Biophysical parameters ? Agricultural practices – Current practices, and alternative practices from more likely to less likely. Biophysical parameters ? Soils and landscapes – Extensive representation, study objectives. Biophysical parameters ?

15 Data Sources Resolution Economic – Cost of production: ERS Resource Regions – Crop Price: NASS state Environmental – SSURGO: MUID – Land use history: county, NRI point (1992) Link – Yield: NASS county – Practices: Tillage (CTIC county), ARMS (ERS Regions) Shrinking agricultural economic databases availability and/or resolution: cost of production, NRI,

16 APAC Budgeting System Provides Consistent, Crop- System Budgets For Research – Critical in Assessing Policy & Environmental Changes Much of The Data Required Comes From Databases Built Into The System

17 ABS Databases Machinery Specifications Prices *** USDA ERS Fertilizer Composition Prices *** USDA NASS Chemical Prices Compatability *** DRPA Inc. Meister Publishing Other Seed Costs *** USDA NASS, Others Irrigation Costs Yield Impacts USDA Farm & Ranch Irrigation Survey Wage Rates By Region USDA NASS

18 ABS Flexibility ABS Supplies The Needs of Several Different Models: – POLYSYS, FLIPSIM, EPIC – ABS Data Are Readily Incorporated Into These Models Has Supported a Range of Research Projects – Sustainable Agriculture, Biomass, Various Biotechnologies, Boll Weevil Eradication

19 ABS Output CAAP

20 Static vs Dynamic Impacts Most implementations imply fix static environmental parameters into economic models While most physical processes occur in the mid or long term, annual/seasonal impacts maybe critical: yield, water However when looking 10, 25 or more years into the future this could be critical Full integration should not be a problem with current computer power

21 POLYSYS Regions (3110 Counties)

22 ALMANAC Developed by ARS-USDA in 1992 to simulate the impact of agronomic decisions on crop biomass production Compiles soil erosion, economic, hydrological, weather, nutrient, plant growth dynamics, and crop management information Simulates plant competition up to 10 crops growing at the same time (unique from EPIC)

23 ALMANAC Does not require local calibration of plant parameters or hydrological components it is ideal for regional-level analyses Has been widely used to estimate yield response to climate and differences in land and water management at a specific location The most recent version of ALMANAC has incorporated additional parameters including evapotranspiration rates and water table information (Kiniry et al., 2005).

24 ALMANAC PRISM weather data Soil layer, landform, and acreage data (SSURGO) Tillage, fertilizer and other management data (ABS) ALMANAC Input File ALMANAC Geodatabase ALMANAC Output: Yield Water: -Precipitation -Transpiration & ET -Potential plant water evaporation -Surface runoff Fertilizer -Loss -Uptake -Mineralize -Fixed Soil erosion Temperature

25 Where Does ALMANAC Fit ? Where Does ALMANAC Fit ? Where Does ALMANAC Fit ? Where Does ALMANAC Fit ? Expected Returns & Available Acreage Acreage Allocation Based on Expected Returns Acreage, Production, Expenditures Export Use Domestic use Production Price Available for Domestic Consumption Food Use Feed Use Bioenery Use Total Use Price Value of Exports & Production Gov’t Payments Cash Receipts Gross & Net Realized Income Production Expenses (U.S.) Crop Demand (U.S.) Crop Demand Livestock ( U.S.) Livestock ( U.S.) Crop Supply (7 /305/ 3110 Regions) Crop Supply (7 /305/ 3110 Regions) (U.S.) Ag Income (U.S.) Ag Income Environmental indicators Land Allocation Decisions Environmental Effects (7/305/3110 Regions) Environmental Effects (7/305/3110 Regions) Daily and monthly weather data (Weather Data) Soil layer, landform, and acreage data (SSURGO) Tillage, fertilizer and other management data (ABS) Crop parameters (USDA-ARS) ALMANAC Input File ALMANAC ALMANAC Output File Geodatabase

26 Final Remarks Data availability and compatibility is one of the major challenges Remote sensing and GIS systems developing new sources of data Use alternative biophysical data based on need, strength, simplicity Processing power, usually not a limiting factor

27 Department of Agricultural Economics, Institute of Agriculture University of Tennessee http://www.agriculture.utk.edu/ Agricultural Policy Analysis Center http://agpolicy.org/ Thanks ! Bio-based Energy Analysis Group http://beag.ag.utk.edu/


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